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Title: A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models, forthcoming at Management Science

 

Speaker: Dr. LI Yang,

Assistant Professor of Marketing, Cheung Kong Graduate School of Business (CKGSB)

 

Date: January 24, 2014 (Friday)

 

Time: 4:30 PM to 5:30 PM

 

Venue: Room 513

       William M.W. Mong Engineering Building (ERB)

       (Engineering Building Complex Phase 2)

       The Chinese University of Hong Kong.

Short Biography of the speaker:

Yang LI is Assistant Professor of Marketing at Cheung Kong Graduate School of Business (CKGSB) in Beijing. He received his Ph.D. from the Business School of Columbia University and had taught undergraduate and MBA classes there. His research focuses on empirical modeling of marketing data, with emphasis on pricing, consumer choice, competitive strategy, and Bayesian nonparametrics. Doctor Li has published on leading academic journals such as Management Science and Journal of Marketing Research, and serves as reviewer for journals such as Operations Research and Management Science. At CKGSB he teaches managerial statistics to MBAs and big data strategy to executive programs, and has consulted for international firms such as Nielsen, Baidu, and Alibaba Group. Prior to joining business academia, Doctor Li earned a bachelor degree in electronics science from Peking University, a master degree in biomedical engineering from Columbia, and worked for United Nations in New York.

 

Abstract:

Marketing variables that are included in consumer discrete choice models are often endogenous. Extant treatments using likelihood-based estimators impose parametric distributional assumptions, such as normality, on the source of endogeneity. These assumptions are restrictive because misspecified distributions have an impact on parameter estimates and associated elasticities. The normality assumption for endogeneity can be inconsistent with some marginal cost specifications given a price-setting process, although they are consistent with other specifications. In this paper, we propose a heterogeneous Bayesian semiparametric approach for modeling choice endogeneity that offers a flexible and robust alternative to parametric methods. Specifically, we construct centered Dirichlet process mixtures (CDPM) to allow uncertainty over the distribution of endogeneity errors. In a similar vein, we also model consumer preference heterogeneity nonparametrically via a CDPM. Results on simulated data show that incorrect distributional assumptions can lead to poor recovery of model parameters and price elasticities, whereas the proposed semiparametric model is able to robustly recover the true parameters in an efficient fashion. In addition, the CDPM offers the benefits of automatically inferring the number of mixture components that are appropriate for a given data set and is able to reconstruct the shape of the underlying distributions for endogeneity and heterogeneity errors. We apply our approach to two scanner panel data sets. Model comparison statistics indicate the superiority of the semiparametric specification and the results show that parameter and elasticity estimates are sensitive to the choice of distributional forms. Moreover, the CDPM specification yields evidence of multimodality, skewness, and outlying observations in these real data sets.

 

                      Everyone is invited to attend the talk.

 

The talk will be hosted by:

Prof. Qi Wu,

Department of Systems Engineering and Engineering Management,

The Chinese University of Hong Kong,

E-mail: qwu@se.cuhk.edu.hk

Telephone Number: (852) 3943-8310

 

For general enquiries, please contact the student coordinator:

Andy Chung,

Department of Systems Engineering and Engineering Management,

The Chinese University of Hong Kong,

E-mail: oychung@se.cuhk.edu.hk

 

SEEM-5202 Website: http://seminar.se.cuhk.edu.hk

Email: seem5202@se.cuhk.edu.hk